Single-Component/Single-Station–Based Machine Learning for Estimating Magnitude and Location of an Earthquake: A Support Vector Machine Approach

Chanda, Sarit; Somala, S.

doi:10.1007/s00024-021-02745-8

Cited by 5 publications

(1 citation statement)

References 29 publications

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“…The main purpose of our analysis of seismic signals is to predict the time left for the next earthquake to occur. However, seismic data are often accompanied by a large amount of noise [22], which may also have an impact on the performance of the prediction model. Therefore, we need to pre-process the seismic dataset first in order to make our pipeline have a stable and efficient prediction performance.…”

Section: A Data Preprocessingmentioning

confidence: 99%

Auto-REP: An Automated Regression Pipeline Approach for High-efficiency Earthquake Prediction Using LANL Data

Yang

Kefalas

Koch

et al. 2022

2022 14th International Conference on Computer and Automation Engineering (ICCAE)

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Earthquake prediction, which is a key issue that has long existed among seismologists, is of high scientific importance. An earthquake prediction model can output the time of earthquake occurrence in advance using machine learning methods, which is receiving increasing attention. Earthquake prediction involves a large variety of data mining steps, which requires a large amount of time for processing data and model development. Thus, an efficient and accurate prediction method is needed. Aiming to solve this problem, we propose Auto-REP, an automated machine learning-based regression model. Our contribution of Auto-REP is using laboratory seismic data to develop a regression pipeline in an automated manner, and eventually obtain the prediction results of laboratory earthquake occurrence. The automated pipeline consists of feature extraction, feature selection, modelling algorithm and optimization. With this approach we extract features from each of the earthquake channels which results in a massive feature space. The hyperparameters of the model are optimized by a Bayesian technique as part of the automated approach. The experimental results shows that the MAE and MSE of our model in the training and testing datasets are 1.48, 1.51 and 1.52, 1.59. The results demonstrate that our Auto-REP method can predict laboratory earthquakes efficiently and accurately.

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Section: A Data Preprocessingmentioning

confidence: 99%